Data from: imageseg: An R package for deep learning-based image segmentation

1. Convolutional neural networks (CNNs) and deep learning are powerful and
robust tools for ecological applications, and are particularly suited for
image data. Image segmentation (the classification of all pixels in
images) is one such application and can for example be used to assess
forest structural metrics. While CNN-based image segmentation methods for
such applications have been suggested, widespread adoption in ecological
research has been slow, likely due to technical difficulties in
implementation of CNNs and lack of toolboxes for ecologists. 2. Here, we
present R package imageseg which implements a CNN-based workflow for
general-purpose image segmentation using the U-Net and U-Net++
architectures in R. The workflow covers data (pre)processing, model
training, and predictions. We illustrate the utility of the package with
image recognition models for two forest structural metrics: tree canopy
density and understory vegetation density. We trained the models using
large and diverse training data sets from a variety of forest types and
biomes, consisting of 2877 canopy images (both canopy cover and
hemispherical canopy closure photographs) and 1285 understory vegetation
images. 3. Overall segmentation accuracy of the models was high with a
Dice score of 0.91 for the canopy model and 0.89 for the understory
vegetation model (assessed with 821 and 367 images, respectively). The
image segmentation models performed significantly better than commonly
used thresholding methods, and generalized well to data from study areas
not included in training. This indicates robustness to variation in input
images and good generalization strength across forest types and biomes. 4.
The package and its workflow allow simple yet powerful assessments of
forest structural metrics using pre-trained models. Furthermore, the
package facilitates custom image segmentation with single or multiple
classes and based on color or grayscale images, e.g. for applications in
cell biology or for medical images. Our package is free, open source, and
available from CRAN. It will enable easier and faster implementation of
deep learning-based image segmentation within R for ecological
applications and beyond.